Data Scientist - Clinical AI

About the role

The A&BC team at CVS Health is dedicated to transforming data into actionable insights that drive growth and improve health outcomes. As a Data Scientist - Clinical AI, you will play a crucial role in leveraging clinical data to enhance decision-making and patient care.

Responsibilities

• Extract signal from unstructured clinical text using NLP techniques.
• Build and fine-tune Small Language Models (SLMs) for clinical use cases.
• Design, train, and evaluate domain-specific SLMs while adhering to compliance requirements.
• Develop predictive analytics solutions using both classical and modern deep learning methods.
• Conduct rigorous Exploratory Data Analysis (EDA) to uncover patterns and inform modeling strategies.
• Communicate findings clearly to technical and non-technical stakeholders through visualizations and presentations.
• Collaborate with various teams to ensure clinical data pipelines support AI/ML workflows and integrate model outputs into products and decision-making processes.
• Stay current with emerging techniques in NLP, foundation models, and clinical AI.

Requirements

• 2+ years of experience in data science, machine learning, or applied NLP, preferably in healthcare.
• Hands-on experience with NLP techniques such as text preprocessing, tokenization, NER, text classification, and topic modeling.
• Practical experience with Large Language Models (LLMs) and/or Small Language Models (SLMs).
• A strong foundation in traditional machine learning methods, including supervised and unsupervised learning, feature engineering, model selection, and performance evaluation.
• Best coding practices, including version control, clean and reproducible code, and organized repositories.
• Deep EDA skills, including systematic dataset exploration, identifying data quality issues, and making informed decisions about modeling approaches.
• Proficiency in Python (pandas, scikit-learn, PyTorch or TensorFlow, Hugging Face Transformers) and SQL for working with large-scale healthcare datasets.
• Experience with cloud-based data and ML platforms, such as Google Cloud Platform (GCP) — BigQuery, Vertex AI, or equivalent.
• Excellent presentation and communication skills, able to explain complex AI/ML concepts to both technical and non-technical audiences.
• Judgment and common sense, understanding when to apply LLMs and when traditional ML methods are more appropriate.
• A genuine curiosity and willingness to learn, staying updated on the latest developments in NLP, foundation models, and clinical AI.

Preferred Qualifications

• Experience working with clinical text data, such as clinical notes, discharge summaries, pathology reports.
• Knowledge of clinical coding systems and terminologies like ICD-10, SNOMED-CT, LOINC, RxNorm, CPT, NDC, UMLS.
• Familiarity with clinical data standards (HL7, FHIR, CCD/C-CDA) and common data models (e.g., OMOP).
• Experience building or contributing to clinical NLP pipelines, including entity extraction, relation extraction, negation detection, or section segmentation from clinical narratives.
• Experience with model evaluation in clinical contexts, understanding sensitivity/specificity tradeoffs, clinical validation, and responsible AI practices in healthcare.
• Familiarity with MLOps practices, such as model versioning, experiment tracking, CI/CD for ML, and model monitoring.
• Experience working directly with clinical stakeholders, tailoring presentations, findings, and recommendations to the appropriate audience level.
• Privacy, security, and compliance experience, including HIPAA/HITRUST, de-identification/tokenization, and PHI handling.